# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch MLCD model.""" import unittest import requests from PIL import Image from transformers import ( AutoProcessor, MLCDVisionConfig, MLCDVisionModel, is_torch_available, ) from transformers.testing_utils import ( require_torch, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor if is_torch_available(): import torch class MLCDVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in MLCD, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return MLCDVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = MLCDVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class MLCDVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Model tester for `MLCDVisionModel`. """ all_model_classes = (MLCDVisionModel,) if is_torch_available() else () test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = MLCDVisionModelTester(self) self.config_tester = ConfigTester(self, config_class=MLCDVisionConfig, has_text_modality=False) def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, torch.nn.Linear)) @require_torch class MLCDVisionModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "DeepGlint-AI/mlcd-vit-bigG-patch14-448" model = MLCDVisionModel.from_pretrained(model_name, attn_implementation="eager").to(torch_device) processor = AutoProcessor.from_pretrained(model_name) # process single image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") # move inputs to the same device as the model inputs = {k: v.to(torch_device) for k, v in inputs.items()} # forward pass with torch.no_grad(): outputs = model(**inputs, output_attentions=True) last_hidden_state = outputs.last_hidden_state last_attention = outputs.attentions[-1] # verify the shapes of last_hidden_state and last_attention self.assertEqual( last_hidden_state.shape, torch.Size([1, 1025, 1664]), ) self.assertEqual( last_attention.shape, torch.Size([1, 16, 1025, 1025]), ) # verify the values of last_hidden_state and last_attention # fmt: off expected_partial_5x5_last_hidden_state = torch.tensor( [ [-0.8978, -0.1181, 0.4769, 0.4761, -0.5779], [ 0.2640, -2.6150, 0.4853, 0.5743, -1.1003], [ 0.3314, -0.3328, -0.4305, -0.1874, -0.7701], [-1.5174, -1.0238, -1.1854, 0.1749, -0.8786], [ 0.2323, -0.8346, -0.9680, -0.2951, 0.0867], ] ).to(torch_device) expected_partial_5x5_last_attention = torch.tensor( [ [2.0930e-01, 6.3073e-05, 1.4717e-03, 2.6881e-05, 3.0513e-03], [1.5828e-04, 2.1056e-03, 4.6784e-04, 1.8276e-03, 5.3233e-04], [5.7824e-04, 1.1446e-03, 1.3854e-03, 1.1775e-03, 1.2750e-03], [9.6343e-05, 1.6365e-03, 2.9066e-04, 3.1089e-03, 2.0607e-04], [6.2688e-04, 1.1656e-03, 1.5030e-03, 8.2819e-04, 2.6992e-03], ] ).to(torch_device) # fmt: on torch.testing.assert_close( last_hidden_state[0, :5, :5], expected_partial_5x5_last_hidden_state, rtol=1e-3, atol=1e-3 ) torch.testing.assert_close( last_attention[0, 0, :5, :5], expected_partial_5x5_last_attention, rtol=1e-4, atol=1e-4 )